Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum

Carvalho, Arnaldo de; Angélico, Bruno Augusto; Justo, J. F.; Oliveira, Alexandre M. de; Filho, João Inácio da Silva

doi:10.1016/j.asoc.2022.109927

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…The symbols present in the given equation are explicitly defined as follows: The symbols M(q), 𝐶(𝑞. 𝑞̇), A, P, u, and λ respectively represent the inertia matrix, Coriolis and centrifugal force vector, constraint matrix, input transform matrix, input torque vector, and Lagrangian multiplier [9][10][11][12][13][14][15][16].…”

Section: ( ) ( ) Tmentioning

confidence: 99%

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A New Method Investigation for Robotic Inverted Pendulum Movement and Control

Dhulkefl,

Mahmood,

Nasret

et al. 2023

JESA

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The integration of sensor fusion techniques has played a crucial role in the advancement of the contemporary robot applications era. This is primarily due to the fact that numerous robot applications heavily rely on the amalgamation of data from multiple sensors, which capture information from the immediate surroundings. Examples of such applications include interactive virtual reality games, navigation systems, and fitness trackers. This study presents the implementation of a sensor fusion technique utilizing low-cost sensors, specifically a gyro sensor and a tilt sensor, to precisely estimate the balancing angle of an inverted pendulum robot system. Hence, this research study presents an innovative approach to address the issue of gyro drift, which has a detrimental impact on the precision of orientation calculations in the indirect Kalman filter-based sensor fusion. The emergence of kinematics and dynamics is observed in this particular process. The frequency responses of two distinct sensors, namely a gyro and a tilt sensor, have been subjected to analysis. A technique of sensor fusion has been utilized to counterbalance the inherent drift of the gyroscope sensor. The integration of a low-cost gyro sensor and a tilt sensor through the process of filtering facilitates the determination of the pendulum angle with efficacy, thereby eliminating the necessity of deploying more expensive sensors. The implementation of control for tracking circular trajectories has been carried out in empirical investigations.

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Section: ( ) ( ) Tmentioning

confidence: 99%

“…Since the gyro sensor works well at high frequencies, it may be used in conjunction with the tilt sensor, which works well at low frequencies. The complementary filter is the result [15,16].…”

Section: Sensing Devices' Frequency Responsesmentioning

confidence: 99%

A New Method Investigation for Robotic Inverted Pendulum Movement and Control

Dhulkefl,

Mahmood,

Nasret

et al. 2023

JESA

View full text Add to dashboard Cite

show abstract

“…Therefore, the application of linear controllers to the IPS depends on the degree of freedom (DOF) and its complex dynamics. During the past few years, different kinds of nonlinear controllers have been applied for IPS control such as nonlinear time-invariant controllers, (sliding mode control (SMC), fuzzy logic control (FLC), [13,15,16], self-learning and adaptive nonlinear controllers, model-free controllers [17], neural network (NN) control [14,20], hybridization of a PID controller and adaptive NN, adaptive NN + PID and adaptive NN + PD controllers [15,18,19], etc. FLC, one of the most used nonlinear controller structures, can approximate any nonlinear control law based on the number of fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review of Different Pendulum Structures in Engineering Applications

Hazem

Bingül

2023

IEEE Access

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For years, the pendulum system has been the most popular experimental setup for education and research in robotics and control theory. This paper examines the different kinematic and dynamic structures of pendulum systems, which are frequently used in literature, considering their usage in various engineering fields. Each pendulum system resembles a real physical system, allowing for a deeper understanding of the system's behavior. While each pendulum has its own advantages and disadvantages, they are subjective and depend on the field of application and method of use. This paper aims to review and simplify the understanding of the various pendulum systems used in research and applications, presenting a summary of the general properties of pendulum systems to the researcher.INDEX TERMS Pendulum systems, robotics, control theory and engineering modelling,

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“…In the study [11]- [29] presenting the method of synthesizing the SMC sliding controller, linear feedback, and Backstepping for this system, the simulation results show the effectiveness of the designed control rules. The control rule design method based on fuzzy control theory and neuron network is presented in the works [30]- [34].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

Chiem,

Thang

2023

Journal of Robotics and Control

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In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system.

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Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum

Cited by 9 publications

References 28 publications

A New Method Investigation for Robotic Inverted Pendulum Movement and Control

A New Method Investigation for Robotic Inverted Pendulum Movement and Control

Comprehensive Review of Different Pendulum Structures in Engineering Applications

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

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